Fr\'echet Distance
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Related Articles from SNS
An Empirical Analysis of Task-Induced Encoder Bias in Fr\'echet Audio Distance
Announce Type: replace-cross Abstract: Fr\'echet Audio Distance (FAD) is the de facto standard for evaluating text-to-audio generation, yet its scores depend on the underlying encoder's embedding space. An encoder's training task dictates which acoustic features are preserved or discarded, causing FAD to inherit systematic task-induced biases. We decompose evaluation into Recall, Precision, and Alignment (split into semantic and structural dimensions), using log-scale normalization for fair...
On Fr\'echet Traveling Salesmen Problems
arXiv:2606.01147v1 Announce Type: new Abstract: The Fr\'echet distance is a well-studied distance measure between two curves. In this work, we demonstrate that the merit of Fr\'echet distance extends beyond evaluating similarity, and introduce a new setting in which it proves useful. Consider a situation where two agents are required to visit a given set of sites, while staying close to each other throughout their traversal.
Conditional Collapse in Sign Language Production: A Diagnostic and a Scaling Argument
arXiv:2606.01643v1 Announce Type: new Abstract: Sign Language Production (SLP) is the task of generating avatar sign language motion from natural language text. The quality of the generated motion is typically evaluated by a motion-space Fr\'echet distance (FID) and back-translation (BT) BLEU score on benchmarks such as How2Sign.
Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding
arXiv:2603.03312v3 Announce Type: replace Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain constrained by three fundamental issues: Semantic Bias, where outputs collapse into generic linguistic templates; Signal Neglect, where models rely heavily on LLM priors to hallucinate fluent text even in the absence of meaningful signals; and the "BLEU Trap", where high-frequency stopwords inflate...
Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding
Announce Type: new Abstract: Symbolic music evaluation for large language models remains fragmented across representations, datasets, and metrics. We introduce LilyBench, a LilyPond-based benchmark that jointly evaluates symbolic music generation and music understanding on the same family of open-weight LLMs. The benchmark includes a 200-prompt generation suite and ten understanding tasks adapted from ABC-Eval, covering syntax, metadata prediction, structural sequencing, and music recognition.
SPADE: Sketch-guided Path Planning Augmented with Diffusion Experts
arXiv:2606.03512v1 Announce Type: new Abstract: Path planning is essential for Autonomous Mobile Robots (AMRs). Conventional methods for incorporating human preferences into planning typically rely on either complex reward engineering or hardware-intensive solutions. Recent state-of-the-art frameworks leverage imitation learning to train behavior-specific path planning models from expert demonstrations.